Data Augmentation in PyTorch

问题

I am a little bit confused about the data augmentation performed in PyTorch. Now, as far as I know, when we are performing data augmentation, we are KEEPING our original dataset, and then adding other versions of it (Flipping, Cropping...etc). But that doesn't seem like happening in PyTorch. As far as I understood from the references, when we use data.transforms in PyTorch, then it applies them one by one. So for example:

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

Here , for the training, we are first randomly cropping the image and resizing it to shape (224,224). Then we are taking these (224,224) images and horizontally flipping them. Therefore, our dataset is now containing ONLY the horizontally flipped images, so our original images are lost in this case.

Am I right? Is this understanding correct? If not, then where do we tell PyTorch in this code above (taken from Official Documentation) to keep the original images and resize them to the expected shape (224,224)?

Thanks

回答1:

The transforms operations are applied to your original images at every batch generation. So your dataset is left unchanged, only the batch images are copied and transformed every iteration.

The confusion may come from the fact that often, like in your example, transforms are used both for data preparation (resizing/cropping to expected dimensions, normalizing values, etc.) and for data augmentation (randomizing the resizing/cropping, randomly flipping the images, etc.).

What your data_transforms['train'] does is:

Randomly resize the provided image and randomly crop it to obtain a (224, 224) patch
Apply or not a random horizontal flip to this patch, with a 50/50 chance
Convert it to a Tensor
Normalize the resulting Tensor, given the mean and deviation values you provided

What your data_transforms['val'] does is:

Resize your image to (256, 256)
Center crop the resized image to obtain a (224, 224) patch
Convert it to a Tensor
Normalize the resulting Tensor, given the mean and deviation values you provided

(i.e. the random resizing/cropping for the training data is replaced by a fixed operation for the validation one, to have reliable validation results)

If you don't want your training images to be horizontally flipped with a 50/50 chance, just remove the transforms.RandomHorizontalFlip() line.

Similarly, if you want your images to always be center-cropped, replace transforms.RandomResizedCrop by transforms.Resize and transforms.CenterCrop, as done for data_transforms['val'].

回答2:

I assume you are asking whether these data augmentation transforms (e.g. RandomHorizontalFlip) actually increase the size of the dataset as well, or are they applied on each item in the dataset one by one and not adding to the size of the dataset.

Running the following simple code snippet we could observe that the latter is true, i.e. if you have a dataset of 8 images, and create a PyTorch dataset object for this dataset when you iterate through the dataset, the transformations are called on each data point, and the transformed data point is returned. So for example if you have random flipping, some of the data points are returned as original, some are returned as flipped (e.g. 4 flipped and 4 original). In other words, by one iteration through the dataset items, you get 8 data points(some flipped and some not). [Which is at odds with the conventional understanding of augmenting the dataset(e.g. in this case having 16 data points in the augmented dataset)]

class experimental_dataset(Dataset):

    def __init__(self, data, transform):
        self.data = data
        self.transform = transform

    def __len__(self):
        return len(self.data.shape[0])

    def __getitem__(self, idx):
        item = self.data[idx]
        item = self.transform(item)
        return item

    transform = transforms.Compose([
        transforms.ToPILImage(),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor()
    ])

x = torch.rand(8, 1, 2, 2)
print(x)

dataset = experimental_dataset(x,transform)

for item in dataset:
    print(item)

Results: (The little differences in floating points are caused by transforming to pil image and back)

Original dummy dataset:

tensor([[[[0.1872, 0.5518],
          [0.5733, 0.6593]]],


    [[[0.6570, 0.6487],
      [0.4415, 0.5883]]],


    [[[0.5682, 0.3294],
      [0.9346, 0.1243]]],


    [[[0.1829, 0.5607],
      [0.3661, 0.6277]]],


    [[[0.1201, 0.1574],
      [0.4224, 0.6146]]],


    [[[0.9301, 0.3369],
      [0.9210, 0.9616]]],


    [[[0.8567, 0.2297],
      [0.1789, 0.8954]]],


    [[[0.0068, 0.8932],
      [0.9971, 0.3548]]]])

transformed dataset:

tensor([[[0.1843, 0.5490],
     [0.5725, 0.6588]]])
tensor([[[0.6549, 0.6471],
     [0.4392, 0.5882]]])
tensor([[[0.5647, 0.3255],
         [0.9333, 0.1216]]])
tensor([[[0.5569, 0.1804],
         [0.6275, 0.3647]]])
tensor([[[0.1569, 0.1176],
         [0.6118, 0.4196]]])
tensor([[[0.9294, 0.3333],
         [0.9176, 0.9608]]])
tensor([[[0.8549, 0.2275],
         [0.1765, 0.8941]]])
tensor([[[0.8902, 0.0039],
         [0.3529, 0.9961]]])

来源：https://stackoverflow.com/questions/51677788/data-augmentation-in-pytorch

标签

python

image-processing

dataset

pytorch

data-augmentation